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多尺度非局部注意力网络的小目标检测算法

1. 1. 黑龙江大学 计算机科学技术学院，哈尔滨 150080
2. 黑龙江大学 黑龙江省数据库与并行计算重点实验室，哈尔滨 150080
3. 黑龙江大学 软件学院，哈尔滨 150080
• 出版日期:2020-10-01 发布日期:2020-10-12

Small Objects Detection Method Based on Multi-scale Non-local Attention Network

LIANG Yanyu, LI Jinbao

1. 1. College of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
2. Key Laboratory of Database and Parallel Computing of Heilongjiang Province, Heilongjiang University, Harbin 150080, China
3. Software Technology Institute, Heilongjiang University, Harbin 150080, China
• Online:2020-10-01 Published:2020-10-12

Abstract:

Existing small objects detection methods usually detect small objects by multi-scale feature maps or multi-scale fusion features. However, these methods mainly utilize the spatial information of the feature maps but ignore the interdependencies between channels. This paper proposes a novel small objects detection network, the network uses the non-local channel attention module to integrate the global spatial information of the features in the shallow layer, and then calibrates the information between channels. And from the spatial domain and the channel domain, it obtains the long-distance dependence of features and enhances the contextual semantic information of small targets in the shallow features. In addition, the network enhances the feature extraction capability of the deep features through dense connection structure, obtains rich target information, and improves the accuracy and real-time of the object detection task. The experimental results show that the algorithm has good detection results on PASCAL VOC and MS COCO datasets, and it can effectively improve the detection accuracy of small objects under the premise of ensuring the detection speed.